NeuroSpec consists of a
number of MATLAB functions for performing multivariate Fourier analysis of time
series and/or point process (spike train or event) data, and plotting the
results. NeuroSpec 2.0 implements
a bivariate spectral analysis, it includes routines for two channel auto
spectral and cross spectral (coherence, phase, cumulant density) analysis, and a
number of extensions including comparison of spectra, a stationarity test for
spectra, comparison of coherence, system identification (gain, phase and impulse
response), and pooled analysis (pooled spectra, pooled coherence, pooled phase,
pooled cumulant density). Pooled analysis incorporates extended difference of
spectra and extended difference of coherence tests. NeuroSpec 2.11
includes extensions for non-parametric directionality analysis of time series and
spike train data. This includes unconditional directionality analysis for two
signals and conditional directionality analysis for three signals. The framework
was designed primarily for use on neural data, but is suited to a wide range of
stationary stochastic (random) signals.

Theoretical and practical aspects of the analysis are described in the following articles:

AcknowledgementsNeuroSpec has been written by David Halliday.
The following people have all contributed to the development of the framework:
Jay Rosenberg, Bernie Conway, Abdul Majeed Amjad, Alex Rigas, David Murray-Smith,
Joe Lau, Peter Breeze, Simon Farmer, Jens Nielsen, Yang Zhan, John-Stuart Brittain, Carl Stevenson, Rob Mason.

Development of NeuroSpec has been supported in part by grants from the
UK Joint Research Council Cognitive Science/HCI Initiative,
The Wellcome Trust (Grants 036928; 048128; 058615),
the UK Engineering and Physical Sciences Research Council (GR/R12350/01),
and the UK Biotechnology and Biological Sciences Research Council (10477).